1. Field of the Invention
The present invention relates to a statistical facial feature extraction method, which uses principle component analysis (PCA) to extract facial features from images.
2. Description of Related Art
With the development of information technology continuously, more and more corresponding applications are introduced into our daily lives for improvement. Especially, the use of effective human-computer interactions makes our lives more convenient and efficient. With recent dramatic decrease in video and image acquisition cost, computer vision systems can be extensively deployed in desktop and embedded systems. For example, an ATM machine can identify users by the images captured from the camera equipped on it, or the video-based access control systems can give the access permission by recognizing captured face images.
Among all the interfaces between humans and computers, a human face is commonly regarded as one of the most efficient media since it carries enormous information (i.e., many facial features like eyes, nose, nostrils, eyebrow, mouth, lip, . . . , etc.), and is most visually discriminative among individuals. Therefore, facial images of individuals can be recognized easier than other kinds of images.
Two typical techniques for facial feature extraction are used: one parameterized model method for describing the facial features based on the energy-minimized values, and the other eigen-image method for detecting facial features.
The former method uses deformable templates to extract desired facial features to change the properties such as size and shape, to match the model to the image and thus obtain more precise description to the facial features. The execution phase uses peak, valley, and edge images as representatives to highlight the salient feature in an image data, and an energy minimization function to alter deformable templates in the image data. The deformable templates are parameterized models for describing the facial features, such as eyes or mouth. Parameter settings can alter the position, orientation, size and other properties of the templates. In addition, an automatic feature detection and age classification system for human face images have developed in the prior art. They represent the shape of eyes or face contour by parametric curves (for example, combination of parabola curves or ovals). Next, an energy function is defined for each facial feature based on its intensity property. For example, a valley can describe the possible location of an iris.
However, the cited method is based on finding the best deformable model capable of minimizing an energy function having the property of the particular facial feature of interest, so deformable model used by the minimization process usually needs a proper initial guess value to help for computing required convergence.
In the other eigen-image method for detecting facial features, a face recognition system is applied to localize desired head and eyes from images in the basis of principal component analysis (PCA) algorithm. For the detection of eyes, typical eigen-eye images are constructed from the basis of eye feature images. To speed up the computational cost, the correlation between an input image and the eigen-template image is computed by Fast Fourier Transform (FFT) algorithm. However, the cited method uses a separate template for comparison, which can only find an individual difference. For example, using a left eye feature image can extract only the corresponding left eye location from a facial image, but cannot detect complete features of a whole face image and is not easy to be matched to statistical models.
Therefore, it is desirable to provide an improved facial feature extraction method to mitigate and/or obviate the aforementioned problems.